An intelligent robot (or, an agent) obtains training data, required for performing a task, through interaction with a human being. A single model may be generated by using the training data obtained by the robot, or segmented models may be generated by using data divided in advance based on human beings or prior knowledge. The generated model is used in regeneration required for the robot to perform a task. The method for obtaining basis skills from the training data about works has been discussed in the non-patent document listed below.
However, in a case where a task is expressed as a single model when generating a model for performing tasks by using the training data, it is not easy to reuse the model. This is because, when it is intended to correct information of the single model or add information to the single model, the entire model should be generated again. Therefore, generating segmented models is more useful for reusing the corresponding model.
In conventional task-performing model generating methods for generating segmented models, training data is segmented based on human beings or prior knowledge, and models are generated based on the training data. However, in case of using human beings or prior knowledge to generate segmented models, the models are generated in various ways depending on individual preferences or prior knowledge, and thus so many sorts of outputs are obtained.
In addition, the model needs to be improved by gradual learning. In the conventional case, a person selects a model to be improved and generates data for improvement continuously and gradually so that the data is used for improving the model. However, the conventional gradual learning methods are just considering a correction of an existing model, without considering an addition of new information while maintaining an existing model.
A model for performing a task of a robot should be capable of being continuously and gradually added and corrected, and a task should be capable of being regenerated based thereon. For this, schemes for performing a task recognition technique and a task regeneration technique simultaneously should be provided so that a robot may perform the task, but the conventional techniques have separately generated and used a model for regenerating a task of a robot and a model for recognizing a task.
However, an intelligent robot should be capable of generating a model for performing a given task under an uncertain environment and regenerating a task based thereon. In addition, the generated model should be easily reused, and information may be corrected or added by using the gradual learning method.
It is highly possible that the environment where a given task should be performed based on a model learned by such an intelligent robot does not perfectly coincide with the learning model. In particular, the chance that the environment perfectly identical to that defined by the model learned in a general working environment or field utilizing an intelligent robot is provide to an intelligent robot is very low. Generally, an intelligent life such as a human being and animal has an ability of flexibly performing a given task based on the already learned knowledge in spite of such a slightly changed environment, and such an application or changing ability is required to an intelligent robot in aspect of technique and tool utilization. In other words, the intelligent robot should also generate a model required for performing a given task under an uncertain environment, and change the model suitably to perform the corresponding task. In particular, even under a situation where the given task changes in real time, the robot should adaptively solve the corresponding task while changing the information based on the learned model.